#### SETUP ####
rm(list = ls())

#load packages
pacman::p_load(tidyverse,
               psych,
               stringi,
               irr)

dt.clean <- read_rds("11-replicate-ratings-RAs/01-data/clean/clean-data")

#### GENDERED INSULTS - Table S21 ####
#### *EXPERIMENT 1 ####
dt.clean %>%
  filter(experiment1 == 1 &
           male == 1) %>%
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 1 &
           female == 1) %>%
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 1 &
           gen.neutral == 1) %>%
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 1 &
           no.name == 1) %>%
  summarise(avg = mean(gendered.insults))

#### *EXPERIMENT 2 ####
dt.clean %>%
  filter(experiment1 == 0 &
           male == 1) %>% 
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 0 &
           female == 1) %>%
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 0 &
           gen.neutral == 1) %>%
  summarise(avg = mean(gendered.insults))

dt.clean %>%
  filter(experiment1 == 0 &
           no.name == 1) %>%
  summarise(avg = mean(gendered.insults))

